It is usually difficult for most customers to specify their requirements when purchasing products or receiving services. A more natural way to identify the customers' requirements is to ask what they really want. Thus, a recommender system, which can assist a user to get a product meeting his/her requirements, is highly desired. However, in practical applications, user experience is ruined by the fact that the recommendation system usually takes a long time to generate a recommendation meeting the users' requirements, because most users are usually not satisfied with an initial recommendation.
This issue has been solved by a Conversational Recommender System (CRS), which allows repeated interactions between the user and the CRS to obtain the right products. One main strategy adopted in the CRSs is to collect the user's requirements, i.e., navigation-by-asking. However, theoretically speaking, it would take an infinite time to navigate the user to all products, thus navigation-by-asking is not practical.
Further, many CRSs adopt a concept of the top k (e.g. top 10) items based on an estimation of the user's preferences. That is, the k highest rated items are presented either as a current recommendation or a potential recommendation. However, the CRSs only provide limited choices to the user, because these recommended products may be similar to each other and far away from an optimal product meeting the user's requirements.
To overcome this issue, some CRSs define a diverse range of preferences. Diversity-oriented CRSs are generally motivated to overcome an imprecision in a preference assessment, but unfortunately fail to quantitatively evaluate the user experience. Intuitively, an ideal CRS is desired to perform like an excellent salesperson, who could focus on the user's feedback and then proactively adjusts his/her selling strategy according to the user's feedback.
The disclosed methods and systems are directed to solve one or more problems set forth above and other problems.